A Systematic Procedure to Identify Human Blood Groups by using Image Processing Assisted Learning Principle

In the occurrence of a life-threatening situation needing the transfer of blood, it is crucial to first determine the patient's blood type. Currently, technicians execute these checks manually, which might introduce room for error. Rapid and accurate blood type determination, free of human error, is crucial. If we use AI methods like artificial intelligence and deep learning to determine a person's blood type, we can minimize the little margin of error introduced by human calculations and outcomes. As the field of computer vision continues to advance at a rapid pace, we are able to provide the highest quality service to our clients. Schematic Learning based Human Blood Group Analyzer (SLHBGA) is a unique deep learning based image processing technology created to determine the human blood types. Methods from the field of image processing, such as filtering and structural processes, are employed. The blood group may be determined rapidly and precisely with this method. The proposed approach of SLHBGA provides better efficiency in results, which will be shown clearly with proper comparisons of conventional learning assisted model called Convolutional Neural Network (CNN) Model. The following resultssection then offers the necessary evidence to demonstrate the effectiveness of the suggested method in terms of following metrics such as: blood group identification accuracy, blood group classification timeline, reduced loss-ratio, sensitivity and specificity.